Independent learners [1]}, [2]} are a class of MARL algorithms that are characterized by intentional obliviousness to the strategic environment: independent learners ignore the presence of other players, effectively treating other players as part of the environment rather than as the non-stationary learning agents that they actually are. By naively running a single-agent reinforcement learning algorithm using only local information, independent learners are relieved of the burden of excessive information, which may lead to scalable algorithms for large-scale decentralized systems. However, additional care must be taken when designing independent learners, as direct application of single-agent reinforcement learning has had mixed success even in small empirical studies [3]}, [4]}, [5]}, [6]}.
